Reliability comparison of power electronic converters used in grid-connected wind energy conversion system
Bibliographic record
Abstract
This work presents a reliability analysis of the power electronic converters for a grid-connected permanent magnet generator-based wind energy conversion system based on the semi conductor power losses. The power converters examined are: the intermediate boost converter (IBC), the intermediate buck-boost converter (IBBC), the back-to-back converter (BBC) and the matrix converter (MC). The aim is to determine which power electronic converter yields the highest mean time between failures (MTBF) and reliability in terms of power losses of the semiconductor devices with a predetermined wind speed. In view of this, a furled wind turbine model developed previously by the author is used to generate power for different wind speeds. Afterwards, a relation between the wind speed, power loss and MTBF is established to evaluate the reliability of the power electronic converters. The power loss model presented in this paper has taken into account the conduction and switching losses of the semiconductor devices within each converter. The analysis reveals that MTBF of an IBC is much higher compared to the other converters considered in this research. The investigation is extended to identify the least reliable component within the converters. It is shown that the inverter has the dominant effect on the system reliability for the converters. This research indicates that IBC with a simple rectifier is a much better option for grid-connected permanent magnet generator based wind energy conversion system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".